This invention generally relates to occupancy detection, estimation and prediction. More particularly, embodiments of this invention relate to predicting occupancy of an enclosure and to systems and methods for detecting occupancy of an enclosure.
Techniques for detecting or sensing occupancy in a structure such as a building is known for a number of applications. For example, an occupancy sensor device attempts to determine if someone is in a room, and is often used in home automation and security systems. Many occupancy sensors that are used for home automation or security systems are based on motion sensors. Motion sensors can be mechanical, for example a simple tripwire, or electronic. Known methods for electronic occupancy detection include acoustical detection and optical detection (including infrared light, visible, laser and radar technology). Motion detectors can process motion-sensor data, or employ cameras connected to a computer which stores and manages captured images to be viewed and analyzed later or viewed over a computer network. Examples of motion detection and sensing applications are (a) detection of unauthorized entry, (b) detection of cessation of occupancy of an area to extinguish lighting and (c) detection of a moving object which triggers a camera to record subsequent events. A motion sensor/detector is thus important for electronic security systems, as well as preventing the wasteful illumination of unoccupied spaces.
Furthermore, some applications can greatly benefit from (even modestly accurate) predictions of future occupancy. For example, heating or cooling of a structure to an acceptable temperature range has an associated lag time of several minutes to more than one hour between actuation and achieving the desired thermal conditions. Therefore it is beneficial to predict with some statistical accuracy, ahead of time, when an occupant or occupants will be entering and/or leaving structure, or moving between different regions or rooms within the structure.
Attempts have been made at predicting occupancy. For example, M. Mozer, L. Vidmar, and R. Dodier, “The Neurothermostat: Predictive Optimal Control of Residential Heating Systems” appearing in M. Mozer et al. Adv. in Neural Info. Proc. Systems 9, (pp. 953-959). Cambridge, Mass.: MIT Press. 1997, discusses a research project in which an occupancy predictor uses a hybrid neural net/look-up table to estimate the probability that an occupant will be home.
According to some embodiments, systems and methods for predicting occupancy of an enclosure are provided. The systems can include a model of occupancy patterns based in part on information of the enclosure and/or the expected occupants of the enclosure, and a sensor adapted to detect occupancy within the enclosure. An occupancy predictor is adapted and programmed to predict future occupancy of the enclosure based at least in part on the model and the occupancy sensor. The model is preferably an a priori stochastic model of human occupancy created prior to installation of the system into the enclosure, and the model preferably includes behavior modeling of activity, itinerary, and/or thermal behavior.
According to some embodiments, the model is based at least in part on the type of the enclosure, with exemplary types including: workplace, single-family home, apartment, and condominium. According to some embodiments, the model is based at least in part on geometrical and structural data about the enclosure.
According to some embodiments, the model is based at least in part on an expected type of occupant of the enclosure. Examples of types of occupant attributes include: age, school enrollment status, marital status, relationships status with other occupants, and retirement status. Examples of expected occupant types include: preschool children, school-age children, seniors, retirees, working-age adults, non-coupled adults, vacationers, office workers, retail store occupants.
According to some embodiments, the model is based in part on seasons of the year and/or the geographic location of the enclosure. The enclosure can be various types of dwellings and/or workplaces.
According to some embodiments, the occupancy prediction of the enclosure is also based in part on user-inputted data, such as occupancy information directly inputted by an occupant of the enclosure, and/or calendar information such as holidays, seasons, weekdays, and weekends.
The occupancy prediction can be used in the actuation and/or control of an HVAC system for the enclosure or various other applications such as: home automation, home security, lighting control, and/or the charging of rechargeable batteries.
According to some embodiments, various systems and methods for detecting occupancy of an enclosure, such as a dwelling, are provided. Examples include: detecting motion, monitoring communication signals such as network traffic and/or mobile phone traffic, monitoring sound pressure information such as in the audible and/or ultrasonic ranges, monitoring utility information such powerline information or information from Smart Meters, monitoring motion in close proximity to the sensor, monitoring infrared signals that tend to indicate operation of infrared controllable devices, sudden changes in ambient light, and monitoring indoor air pressure (to distinguish from pressure mats used in security applications) information which tends to indicate occupancy.
According to some embodiments, the occupancy predictor includes one or more algorithms for predicting occupancy based on one or more occupancy patterns, and the occupancy predictions are based in part on a maximum-likelihood approach.
As used herein the term “model” refers generally to a description or representation of a system. The description or representation can use mathematical language, such as in the case of mathematical models. Examples of types of models and/or characteristics of models, without limitation, include: lookup tables, linear, non-linear, deterministic, probabilistic, static, dynamic, and models having lumped parameters and/or distributed parameters.
As used herein the term “sensor” refers generally to a device or system that measures and/or registers a substance, physical phenomenon and/or physical quantity. The sensor may convert a measurement into a signal, which can be interpreted by an observer, instrument and/or system. A sensor can be implemented as a special purpose device and/or can be implemented as software running on a general-purpose computer system.
It will be appreciated that these systems and methods are novel, as are applications thereof and many of the components, systems, methods and algorithms employed and included therein. It should be appreciated that embodiments of the presently described inventive body of work can be implemented in numerous ways, including as processes, apparata, systems, devices, methods, computer readable media, computational algorithms, embedded or distributed software and/or as a combination thereof. Several illustrative embodiments are described below.